The problems studied in the department can be subsumed under the
heading of empirical inference, i.e., inference
performed on the basis of empirical data. This includes statistical learning,
but also the inference of
causal structures from statistical data, leading to models that
provide insight into the underlying mechanisms, and make predictions
about the effect of interventions. Likewise, the type of empirical
data can vary, ranging from biological measurements (e.g., in neuroscience) to astronomical observations. We are conducting
theoretical, algorithmic, and experimental studies to try and
understand the problem of empirical inference.

The department was started around statistical learning theory and kernel methods. It has since broadened
its set of inference tools to include a stronger component of
Bayesian methods, including graphical models with a recent focus on
issues of causality. In terms of the inference tasks being studied,
we have moved towards tasks that go beyond the relatively
well-studied problem of supervised learning, such as semi-supervised
learning or transfer learning. Finally, we have continuously
striven to analyze challenging datasets from biology, astronomy, and
other domains, leading to the inclusion of several application areas
in our portfolio.
No matter whether the
applications are done in the department or in collaboration with
external partners, considering a whole range of applications helps
us study principles and methods of inference, rather than
inference applied to one specific problem domain.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems